Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/wellapp-ai/well/tech-divergencenpx skills add WellApp-ai/Well --skill tech-divergencegit clone --depth 1 https://github.com/WellApp-ai/WellWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/wellapp-ai/well/tech-divergence)<a href="https://agentmods.dev/skills/wellapp-ai/well/tech-divergence"><img src="https://agentmods.dev/badge/skills/wellapp-ai/well/tech-divergence.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00019 | $0.01360 |
| Opus 5 | $0.00010 | $0.00680 |
| Sonnet 5 | $0.00004 | $0.00272 |
| Haiku 4.5 | $0.00002 | $0.00136 |
Grade A, and why
tech-divergence scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tech Divergence Skill
Evaluate technical implementation options using an 8-dimension scoring matrix. Low scores auto-proceed; high scores (>= 4) trigger Gate 4 for human decision.
When to Use
- During Plan Mode Phase 2 (Technical Diverge)
- Before committing to a specific architecture or pattern
- When multiple valid implementation approaches exist
Scoring Matrix (8 Dimensions)
Each dimension scores 0 (auto-proceed) or 1 (adds to checkpoint score):
| Dimension | 0 (Low Risk) | 1 (Checkpoint) |
|---|---|---|
| Pattern | Exists in codebase | New pattern required |
| Scope | Single domain | Cross-domain impact |
| Data Model | Add field to existing | New entity/table |
| Dependencies | Use existing libs | New dependency |
| API Surface | Internal only | Public/breaking change |
| Reversibility | Easy to undo | Requires migration |
| Security | Non-sensitive data | Auth/permissions |
| Performance | Simple CRUD | Cache/queue/optimization |
Phase 1: Gather Context
1.1 Query Pattern Library (Notion)
Check if similar patterns exist:
API-query-database:
database_id: "[PATTERN_LIBRARY_DB_ID]"
filter:
property: "Domain"
select:
equals: "[current domain]"
1.2 Search Codebase
SemanticSearch: "How is [similar feature] implemented?"
Grep: "[pattern name]" in relevant directories
1.3 Query Context7 (External Libraries)
If new libraries are being considered:
Context7 MCP:
1. resolve-library-id: libraryName = "[library]"
2. get-library-docs: topic = "best practices", mode = "info"
Phase 2: Score Each Dimension
For each of the 8 dimensions, evaluate and score:
## Technical Divergence Score
| Dimension | Score | Rationale |
|-----------|-------|-----------|
| Pattern | 0/1 | [Exists/New] |
| Scope | 0/1 | [Single/Cross-domain] |
| Data Model | 0/1 | [Field/Entity] |
| Dependencies | 0/1 | [Existing/New] |
| API Surface | 0/1 | [Internal/Public] |
| Reversibility | 0/1 | [Easy/Migration] |
| Security | 0/1 | [Non-sensitive/Auth] |
| Performance | 0/1 | [CRUD/Optimization] |
| **TOTAL** | [0-8] | |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 207 lines · 19 tokens per session scan A 65c11451a1a4
tech-divergence is a skill published in the GitHub repository WellApp-ai/Well (340 stars, last pushed 29d ago), licensed MIT. It adds 19 tokens to every session and 1,360 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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